In Multi-access Edge Computing (MEC) systems, service migration has been extensively applied to ensure service quality by migrating services to follow mobile users. The existing migration methods mainly focus on optimizing service response latency and migration costs by predicting user’s movements. However, some malicious adversaries can learn auxiliary knowledge, i.e., users’ mobility model and service migration trajectory, and launch location inference attacks to infer user locations. This leads to serious personal security threats, like malvertising, fraud and kidnapping. In this paper, we propose a location privacy-aware service migration method to against adversaries’ location inference attacks in multi-user MEC systems. First, we adopt an entropy-based location privacy metric to accurately measure user’s location privacy leakage risk. Then, we formulate the service migration progress as a joint optimization problem that minimizes service response latency and location privacy leakage risk. To cope with inter-user interference, we developed a multi-agent soft actor-critic (MASAC) algorithm to help users collaboratively make service migration decisions. Finally, simulations based on real-world user movement trajectories were conducted to demonstrate the superiority of the proposed method. Evaluation and analysis results showed that our proposed method can effectively protect user location privacy while maintaining a low service response latency. 

Location Privacy-aware Service Migration against Inference Attacks in Multi-user MEC Systems

This article explores the causes of the issues with personal data privacy and outlines the limitations of China's current legal framework. This article makes the argument that, in the information age, self-discipline and legal protection should be combined in order to safeguard personal information safety. It also makes specific recommendations for strengthening legal protection. This research also develops a data processing platform for data safety and privacy protection while studying the technology of data safety environment monitoring and privacy protection. This work develops optimization methodologies, such as dynamic privacy budget allocation, to increase the model's speed of convergence and the calibre of the generated data. It adjusts to various privacy and timeliness needs under the assumption that the objective of selective matching of private safety will be satisfied. According to the experimental findings, this algorithm's accuracy can reach 96.27%. This method enhances the model's speed of convergence and the calibre of the data created, and it addresses the flaw that the present data fusion publishing procedure cannot withstand the attack of background information. The study's findings can serve as a starting point for future work on data security and the protection of personal information.

/pdf/personal-information-security-environment-monitoring-and-law-2hn8vqet.pdf

Personal Information Security Environment Monitoring and Law Protection Using Big Data Analysis

With the development of Intelligent Transportation Systems (ITS), a vast amount of location data is being generated from various IoT devices equipped with location positioning sensors. Preserving the privacy of location data release is a critical concern, as the publication of aggregated data often reveals private information about the users. Differential Privacy (DP) has recently emerged as a robust framework to guarantee privacy in this context. However, conventional DP mechanisms commonly make no assumption about the distribution of the input data, which could lead to unexpected privacy leakage if the data are correlated. In this paper, we investigate the complex simultaneous impact of user correlation, spatial–temporal correlation and prior knowledge of an adversary on the privacy leakage of a DP mechanism, which has not been addressed in prior work. We derive several closed-form expressions that demonstrate and quantify the privacy leakage under correlated location data, followed by the design of efficient algorithms to compute such privacy leakage. Then, we propose a Δ-CDP (Correlated Differential Privacy) to provide a formal privacy guarantee against the additional privacy leakage incurred by these factors. Extensive comparisons, theoretical analysis, and experimental simulations are presented to validate the correctness and efficiency of the proposed work. 

May the privacy be with us: Correlated differential privacy in location data for ITS

This article explores the causes of the issues with personal data privacy and outlines the limitations of China ’ s current legal framework. This article makes the argument that, in the information age, self-discipline and legal protection should be combined in order to safeguard personal information safety. It also makes speci ﬁ c recommendations for strengthening legal protection. This research also develops a data processing platform for data safety and privacy protection while studying the technology of data safety environment monitoring and privacy protection. This work develops optimization methodologies, such as dynamic privacy budget allocation, to increase the model ’ s speed of convergence and the calibre of the generated data. It adjusts to various privacy and timeliness needs under the assumption that the objective of selective matching of private safety will be satis ﬁ ed. According to the experimental ﬁ ndings, this algorithm ’ s accuracy can reach 96.27%. This method enhances the model ’ s speed of convergence and the calibre of the data created, and it addresses the ﬂ aw that the present data fusion publishing procedure cannot withstand the attack of background information. The study ’ s ﬁ ndings can serve as a starting point for future work on data security and the protection of personal information.

Local Differential Privacy for correlated location data release in ITS

The rapid development of the Global Positioning System (GPS) devices and location-based services (LBSs) facilitates the collection of huge amounts of personal information for the untrusted/unknown LBS providers This phenomenon raises serious privacy concerns However, most of the existing solutions aim at locating interference in the static scenes or in a single timestamp without considering the correlation between location transfer and time of moving users In this way, the solutions are vulnerable to various inference attacks Traditional privacy protection methods rely on trusted third-party service providers, but in reality, we are not sure whether the third party is trustable In this paper, we propose a systematic solution to preserve location information The protection provides a rigorous privacy guarantee without the assumption of the credibility of the third parties The user’s historical trajectory information is used as the basis of the hidden Markov model prediction, and the user’s possible prospective location is used as the model output result to protect the user’s trajectory privacy To formalize the privacy-protecting guarantee, we propose a new definition, L&A-location region, based on - anonymity and differential privacy Based on the proposed privacy definition, we design a novel mechanism to provide a privacy protection guarantee for the users’ identity trajectory We simulate the proposed mechanism based on a dataset collected in real practice The result of the simulation shows that the proposed algorithm can provide privacy protection to a high standard

/pdf/protecting-the-moving-user-s-locations-by-combining-5gf3yqtjo0.pdf

Protecting the Moving User’s Locations by Combining Differential Privacy and k -Anonymity under Temporal Correlations in Wireless Networks

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Protecting the Moving User’s Locations by Combining Differential Privacy and k -Anonymity under Temporal Correlations in Wireless Networks